Overview

Dataset statistics

Number of variables20
Number of observations224
Missing cells67
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.1 KiB
Average record size in memory160.6 B

Variable types

Numeric17
Categorical3

Alerts

Experimental_Year is highly overall correlated with DateHigh correlation
SDM is highly overall correlated with SFMHigh correlation
SFM is highly overall correlated with SDMHigh correlation
Total_Nitrogen is highly overall correlated with CN_RatioHigh correlation
CN_Ratio is highly overall correlated with Total_NitrogenHigh correlation
Sodium is highly overall correlated with BeneficialsHigh correlation
Sulphur is highly overall correlated with CropHigh correlation
Date is highly overall correlated with Experimental_Year and 1 other fieldsHigh correlation
Crop is highly overall correlated with Sulphur and 1 other fieldsHigh correlation
Beneficials is highly overall correlated with SodiumHigh correlation
Sodium has 64 (28.6%) missing valuesMissing
Experimental_Year is highly skewed (γ1 = 1.366897501)Skewed
Total_Carbon is highly skewed (γ1 = -1.308036543)Skewed
Calcium is highly skewed (γ1 = 1.494173564)Skewed
Copper is highly skewed (γ1 = 1.228070075)Skewed
Magnesium is highly skewed (γ1 = 1.836653314)Skewed
Manganese is highly skewed (γ1 = 1.183970177)Skewed
Sodium is highly skewed (γ1 = 1.293712088)Skewed
Sulphur is highly skewed (γ1 = 1.432047061)Skewed

Reproduction

Analysis started2024-07-17 12:46:04.191496
Analysis finished2024-07-17 12:46:50.555228
Duration46.36 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Experimental_Year
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness1.37
Mean2.02 × 103
Minimum2.02 × 103
Maximum2.02 × 103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:50.609860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.02 × 103
5-th percentile2.02 × 103
Q12.02 × 103
median2.02 × 103
Q32.02 × 103
95-th percentile2.02 × 103
Maximum2.02 × 103
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.73
Coefficient of variation (CV)0.000362
Kurtosis0.266
Mean2.02 × 103
Median Absolute Deviation (MAD)0
Skewness1.37
Sum4.52 × 105
Variance0.533
MonotonicityIncreasing
2024-07-17T14:46:50.740062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=3)
ValueCountFrequency (%)
2019 160
71.4%
2020 32
 
14.3%
2021 32
 
14.3%
ValueCountFrequency (%)
2019 160
71.4%
2020 32
 
14.3%
2021 32
 
14.3%
ValueCountFrequency (%)
2021 32
 
14.3%
2020 32
 
14.3%
2019 160
71.4%

Date
Categorical

Distinct6
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2019-05-21 00:00:00.0000000
48 
2019-05-08 00:00:00.0000000
48 
2019-05-22 00:00:00.0000000
48 
2020-07-15 00:00:00.0000000
32 
2021-07-26 00:00:00.0000000
32 

Length

Max length27
Median length27
Mean length27
Min length27

Characters and Unicode

Total characters6048
Distinct characters12
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-05-21 00:00:00.0000000
2nd row2019-05-21 00:00:00.0000000
3rd row2019-05-21 00:00:00.0000000
4th row2019-05-21 00:00:00.0000000
5th row2019-05-21 00:00:00.0000000

Common Values

ValueCountFrequency (%)
2019-05-21 00:00:00.0000000 48
21.4%
2019-05-08 00:00:00.0000000 48
21.4%
2019-05-22 00:00:00.0000000 48
21.4%
2020-07-15 00:00:00.0000000 32
14.3%
2021-07-26 00:00:00.0000000 32
14.3%
2019-07-02 00:00:00.0000000 16
 
7.1%

Length

2024-07-17T14:46:50.893707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T14:46:51.068168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
00:00:00.0000000 224
50.0%
2019-05-21 48
 
10.7%
2019-05-08 48
 
10.7%
2019-05-22 48
 
10.7%
2020-07-15 32
 
7.1%
2021-07-26 32
 
7.1%
2019-07-02 16
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 3456
57.1%
2 480
 
7.9%
- 448
 
7.4%
: 448
 
7.4%
1 272
 
4.5%
224
 
3.7%
. 224
 
3.7%
5 176
 
2.9%
9 160
 
2.6%
7 80
 
1.3%
Other values (2) 80
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4704
77.8%
Other Punctuation 672
 
11.1%
Dash Punctuation 448
 
7.4%
Space Separator 224
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3456
73.5%
2 480
 
10.2%
1 272
 
5.8%
5 176
 
3.7%
9 160
 
3.4%
7 80
 
1.7%
8 48
 
1.0%
6 32
 
0.7%
Other Punctuation
ValueCountFrequency (%)
: 448
66.7%
. 224
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 448
100.0%
Space Separator
ValueCountFrequency (%)
224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3456
57.1%
2 480
 
7.9%
- 448
 
7.4%
: 448
 
7.4%
1 272
 
4.5%
224
 
3.7%
. 224
 
3.7%
5 176
 
2.9%
9 160
 
2.6%
7 80
 
1.3%
Other values (2) 80
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3456
57.1%
2 480
 
7.9%
- 448
 
7.4%
: 448
 
7.4%
1 272
 
4.5%
224
 
3.7%
. 224
 
3.7%
5 176
 
2.9%
9 160
 
2.6%
7 80
 
1.3%
Other values (2) 80
 
1.3%

Plot_ID
Real number (ℝ)

Distinct160
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness0.136
Mean108
Minimum1
Maximum232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:51.229936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q156.8
median99
Q3168
95-th percentile221
Maximum232
Range231
Interquartile range (IQR)112

Descriptive statistics

Standard deviation67.6
Coefficient of variation (CV)0.628
Kurtosis-1.09
Mean108
Median Absolute Deviation (MAD)64
Skewness0.136
Sum2.41 × 104
Variance4.57 × 103
MonotonicityNot monotonic
2024-07-17T14:46:51.361488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
179 3
 
1.3%
87 3
 
1.3%
101 3
 
1.3%
99 3
 
1.3%
98 3
 
1.3%
97 3
 
1.3%
162 3
 
1.3%
163 3
 
1.3%
164 3
 
1.3%
88 3
 
1.3%
Other values (150) 194
86.6%
ValueCountFrequency (%)
1 1
 
0.4%
2 1
 
0.4%
3 1
 
0.4%
4 1
 
0.4%
5 3
1.3%
ValueCountFrequency (%)
232 1
0.4%
231 1
0.4%
230 1
0.4%
229 1
0.4%
228 1
0.4%

Crop
Categorical

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Winter wheat
96 
Grain maize
80 
Winter rapeseed
48 

Length

Max length15
Median length12
Mean length12.3
Min length11

Characters and Unicode

Total characters2752
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter wheat
2nd rowWinter wheat
3rd rowWinter wheat
4th rowWinter wheat
5th rowWinter wheat

Common Values

ValueCountFrequency (%)
Winter wheat 96
42.9%
Grain maize 80
35.7%
Winter rapeseed 48
21.4%

Length

2024-07-17T14:46:51.517159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T14:46:51.708652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
winter 144
32.1%
wheat 96
21.4%
grain 80
17.9%
maize 80
17.9%
rapeseed 48
 
10.7%

Most occurring characters

ValueCountFrequency (%)
e 464
16.9%
i 304
11.0%
a 304
11.0%
r 272
9.9%
t 240
8.7%
n 224
8.1%
224
8.1%
W 144
 
5.2%
w 96
 
3.5%
h 96
 
3.5%
Other values (6) 384
14.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2304
83.7%
Space Separator 224
 
8.1%
Uppercase Letter 224
 
8.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 464
20.1%
i 304
13.2%
a 304
13.2%
r 272
11.8%
t 240
10.4%
n 224
9.7%
w 96
 
4.2%
h 96
 
4.2%
m 80
 
3.5%
z 80
 
3.5%
Other values (3) 144
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
W 144
64.3%
G 80
35.7%
Space Separator
ValueCountFrequency (%)
224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2528
91.9%
Common 224
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 464
18.4%
i 304
12.0%
a 304
12.0%
r 272
10.8%
t 240
9.5%
n 224
8.9%
W 144
 
5.7%
w 96
 
3.8%
h 96
 
3.8%
G 80
 
3.2%
Other values (5) 304
12.0%
Common
ValueCountFrequency (%)
224
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 464
16.9%
i 304
11.0%
a 304
11.0%
r 272
9.9%
t 240
8.7%
n 224
8.1%
224
8.1%
W 144
 
5.2%
w 96
 
3.5%
h 96
 
3.5%
Other values (6) 384
14.0%

Beneficials
Categorical

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Control
192 
BMs
32 

Length

Max length7
Median length7
Mean length6.43
Min length3

Characters and Unicode

Total characters1440
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowControl
2nd rowControl
3rd rowControl
4th rowControl
5th rowControl

Common Values

ValueCountFrequency (%)
Control 192
85.7%
BMs 32
 
14.3%

Length

2024-07-17T14:46:51.855966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-17T14:46:52.024495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
control 192
85.7%
bms 32
 
14.3%

Most occurring characters

ValueCountFrequency (%)
o 384
26.7%
C 192
13.3%
n 192
13.3%
t 192
13.3%
r 192
13.3%
l 192
13.3%
B 32
 
2.2%
M 32
 
2.2%
s 32
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1184
82.2%
Uppercase Letter 256
 
17.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 384
32.4%
n 192
16.2%
t 192
16.2%
r 192
16.2%
l 192
16.2%
s 32
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 192
75.0%
B 32
 
12.5%
M 32
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1440
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 384
26.7%
C 192
13.3%
n 192
13.3%
t 192
13.3%
r 192
13.3%
l 192
13.3%
B 32
 
2.2%
M 32
 
2.2%
s 32
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 384
26.7%
C 192
13.3%
n 192
13.3%
t 192
13.3%
r 192
13.3%
l 192
13.3%
B 32
 
2.2%
M 32
 
2.2%
s 32
 
2.2%

SDM
Real number (ℝ)

Distinct126
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness0.274
Mean276
Minimum68.5
Maximum760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:52.140121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum68.5
5-th percentile75.7
Q196.5
median315
Q3423
95-th percentile514
Maximum760
Range691
Interquartile range (IQR)326

Descriptive statistics

Standard deviation173
Coefficient of variation (CV)0.627
Kurtosis-1.06
Mean276
Median Absolute Deviation (MAD)189
Skewness0.274
Sum6.18 × 104
Variance2.99 × 104
MonotonicityNot monotonic
2024-07-17T14:46:52.259296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
83.03 3
 
1.3%
405.3 3
 
1.3%
340.7 3
 
1.3%
393.8 3
 
1.3%
368 3
 
1.3%
435.3 3
 
1.3%
345.7 3
 
1.3%
71.43 3
 
1.3%
76.63 3
 
1.3%
119.76 3
 
1.3%
Other values (116) 194
86.6%
ValueCountFrequency (%)
68.54 3
1.3%
71.43 3
1.3%
72.85 3
1.3%
75.5 3
1.3%
76.63 3
1.3%
ValueCountFrequency (%)
759.5 1
0.4%
723.4 1
0.4%
702.4 1
0.4%
655 1
0.4%
628.2 1
0.4%

SFM
Real number (ℝ)

Distinct128
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness0.797
Mean1.48 × 103
Minimum16.1
Maximum5.4 × 103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:52.391471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum16.1
5-th percentile306
Q1426
median1.61 × 103
Q32.28 × 103
95-th percentile3.21 × 103
Maximum5.4 × 103
Range5.39 × 103
Interquartile range (IQR)1.85 × 103

Descriptive statistics

Standard deviation1.12 × 103
Coefficient of variation (CV)0.753
Kurtosis0.551
Mean1.48 × 103
Median Absolute Deviation (MAD)1.07 × 103
Skewness0.797
Sum3.33 × 105
Variance1.25 × 106
MonotonicityNot monotonic
2024-07-17T14:46:52.523722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
349.6 3
 
1.3%
2280.8 3
 
1.3%
1666.3 3
 
1.3%
1937.1 3
 
1.3%
2163.5 3
 
1.3%
2467.7 3
 
1.3%
1729.1 3
 
1.3%
332.4 3
 
1.3%
365.6 3
 
1.3%
538.6 3
 
1.3%
Other values (118) 194
86.6%
ValueCountFrequency (%)
16.06470495 3
1.3%
20.76862238 3
1.3%
297.5 3
1.3%
305.3 3
1.3%
312.9 3
1.3%
ValueCountFrequency (%)
5403.2 1
0.4%
5360.8 1
0.4%
5129 1
0.4%
4516.4 1
0.4%
4248.8 1
0.4%

Total_Carbon
Real number (ℝ)

Distinct123
Distinct (%)55.2%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Skewness-1.31
Mean440
Minimum391
Maximum459
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:52.649656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum391
5-th percentile400
Q1440
median450
Q3453
95-th percentile455
Maximum459
Range68.3
Interquartile range (IQR)12.3

Descriptive statistics

Standard deviation20.1
Coefficient of variation (CV)0.0458
Kurtosis-0.0251
Mean440
Median Absolute Deviation (MAD)3.63
Skewness-1.31
Sum9.81 × 104
Variance406
MonotonicityNot monotonic
2024-07-17T14:46:52.775401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
446.5904045 3
 
1.3%
395.8243752 3
 
1.3%
401.6290855 3
 
1.3%
406.8119621 3
 
1.3%
406.3415527 3
 
1.3%
406.1695862 3
 
1.3%
408.7385941 3
 
1.3%
450.9137535 3
 
1.3%
452.8623772 3
 
1.3%
454.1836166 3
 
1.3%
Other values (113) 193
86.2%
ValueCountFrequency (%)
390.5019188 3
1.3%
395.8243752 3
1.3%
397.0729828 3
1.3%
399.984436 3
1.3%
400.6892014 3
1.3%
ValueCountFrequency (%)
458.8500214 1
 
0.4%
457.5219917 1
 
0.4%
457.2322655 1
 
0.4%
456.4206886 3
1.3%
455.8024979 3
1.3%

Total_Nitrogen
Real number (ℝ)

Distinct125
Distinct (%)56.1%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Skewness-0.21
Mean34.2
Minimum19.8
Maximum44.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:52.913065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum19.8
5-th percentile24.6
Q129.9
median34.6
Q338.8
95-th percentile42.6
Maximum44.5
Range24.7
Interquartile range (IQR)8.9

Descriptive statistics

Standard deviation5.71
Coefficient of variation (CV)0.167
Kurtosis-0.716
Mean34.2
Median Absolute Deviation (MAD)4.29
Skewness-0.21
Sum7.63 × 103
Variance32.5
MonotonicityNot monotonic
2024-07-17T14:46:53.047490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
34.62292552 3
 
1.3%
31.90737844 3
 
1.3%
32.91337848 3
 
1.3%
26.05662704 3
 
1.3%
39.80962873 3
 
1.3%
34.1245687 3
 
1.3%
37.08785534 3
 
1.3%
41.95408583 3
 
1.3%
42.66485929 3
 
1.3%
41.84370041 3
 
1.3%
Other values (115) 193
86.2%
ValueCountFrequency (%)
19.84923005 3
1.3%
23.5 1
 
0.4%
23.59727263 3
1.3%
23.9 1
 
0.4%
24.1 1
 
0.4%
ValueCountFrequency (%)
44.51313257 3
1.3%
43.02977562 3
1.3%
42.99328327 3
1.3%
42.66485929 3
1.3%
42.39742994 3
1.3%

CN_Ratio
Real number (ℝ)

Distinct127
Distinct (%)57.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Skewness0.828
Mean13.2
Minimum10.2
Maximum20.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:53.177448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10.2
5-th percentile10.6
Q111.2
median12.7
Q314.6
95-th percentile17.3
Maximum20.3
Range10.1
Interquartile range (IQR)3.38

Descriptive statistics

Standard deviation2.24
Coefficient of variation (CV)0.17
Kurtosis0.179
Mean13.2
Median Absolute Deviation (MAD)1.61
Skewness0.828
Sum2.95 × 103
Variance5.02
MonotonicityNot monotonic
2024-07-17T14:46:53.316529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
12.89869147 3
 
1.3%
12.40756001 3
 
1.3%
12.20306297 3
 
1.3%
15.6127406 3
 
1.3%
10.20714109 3
 
1.3%
11.90326115 3
 
1.3%
11.02188921 3
 
1.3%
10.74779016 3
 
1.3%
10.61441074 3
 
1.3%
10.85430043 3
 
1.3%
Other values (117) 193
86.2%
ValueCountFrequency (%)
10.20714109 3
1.3%
10.23972519 3
1.3%
10.52291534 3
1.3%
10.56599679 3
1.3%
10.61441074 3
1.3%
ValueCountFrequency (%)
20.29173764 3
1.3%
18.83404255 1
 
0.4%
18.39748954 1
 
0.4%
18.15767635 1
 
0.4%
18.0242915 1
 
0.4%

Calcium
Real number (ℝ)

Distinct127
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness1.49
Mean12.7
Minimum3.94
Maximum46.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:53.441867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.94
5-th percentile4.55
Q15.16
median5.7
Q36.66
95-th percentile44.4
Maximum46.6
Range42.7
Interquartile range (IQR)1.49

Descriptive statistics

Standard deviation14.1
Coefficient of variation (CV)1.11
Kurtosis0.398
Mean12.7
Median Absolute Deviation (MAD)0.665
Skewness1.49
Sum2.85 × 103
Variance198
MonotonicityNot monotonic
2024-07-17T14:46:53.554136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5.218285844 3
 
1.3%
45.8266423 3
 
1.3%
42.80622118 3
 
1.3%
31.98566284 3
 
1.3%
42.26696504 3
 
1.3%
35.51186381 3
 
1.3%
36.10318018 3
 
1.3%
5.78994285 3
 
1.3%
5.48445572 3
 
1.3%
6.124677173 3
 
1.3%
Other values (117) 194
86.6%
ValueCountFrequency (%)
3.937825839 1
 
0.4%
4.148807833 1
 
0.4%
4.175666645 1
 
0.4%
4.257519341 1
 
0.4%
4.342555554 3
1.3%
ValueCountFrequency (%)
46.59051317 3
1.3%
45.8266423 3
1.3%
45.31534485 3
1.3%
44.63354357 3
1.3%
42.80622118 3
1.3%

Copper
Real number (ℝ)

Distinct125
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness1.23
Mean6.12
Minimum2.62
Maximum21.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:53.671041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.62
5-th percentile2.85
Q13.75
median5.61
Q38.18
95-th percentile10.5
Maximum21.6
Range19
Interquartile range (IQR)4.42

Descriptive statistics

Standard deviation2.74
Coefficient of variation (CV)0.447
Kurtosis3.45
Mean6.12
Median Absolute Deviation (MAD)2.03
Skewness1.23
Sum1.37 × 103
Variance7.49
MonotonicityNot monotonic
2024-07-17T14:46:53.794445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2.94253166 3
 
1.3%
4.384043134 3
 
1.3%
3.786231884 3
 
1.3%
3.277905802 3
 
1.3%
4.655206436 3
 
1.3%
2.947905729 3
 
1.3%
3.021553286 3
 
1.3%
7.636200954 3
 
1.3%
6.456921712 3
 
1.3%
5.162688698 3
 
1.3%
Other values (115) 194
86.6%
ValueCountFrequency (%)
2.622762813 3
1.3%
2.773977087 3
1.3%
2.784681169 3
1.3%
2.83926623 3
1.3%
2.94253166 3
1.3%
ValueCountFrequency (%)
21.59785166 1
0.4%
13.98599302 1
0.4%
12.54900304 1
0.4%
12.05492424 1
0.4%
11.94662574 1
0.4%

Iron
Real number (ℝ)

Distinct116
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness0.916
Mean83.8
Minimum50.6
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:53.936063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50.6
5-th percentile53.7
Q161.4
median82.7
Q399.8
95-th percentile120
Maximum177
Range127
Interquartile range (IQR)38.3

Descriptive statistics

Standard deviation24.1
Coefficient of variation (CV)0.288
Kurtosis1.37
Mean83.8
Median Absolute Deviation (MAD)20
Skewness0.916
Sum1.88 × 104
Variance582
MonotonicityNot monotonic
2024-07-17T14:46:54.080475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
51.48801447 3
 
1.3%
111.1223895 3
 
1.3%
85.11380138 3
 
1.3%
115.217036 3
 
1.3%
105.37196 3
 
1.3%
104.4327164 3
 
1.3%
65.57285139 3
 
1.3%
66.99458066 3
 
1.3%
61.14013231 3
 
1.3%
59.30131467 3
 
1.3%
Other values (106) 194
86.6%
ValueCountFrequency (%)
50.62353488 3
1.3%
51.48801447 3
1.3%
51.9937726 3
1.3%
53.64075535 3
1.3%
53.82581763 3
1.3%
ValueCountFrequency (%)
177.4614381 3
1.3%
138.1944544 1
 
0.4%
132.8168601 1
 
0.4%
132.1451931 1
 
0.4%
125.6682347 1
 
0.4%

Magnesium
Real number (ℝ)

Distinct122
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness1.84
Mean1.95
Minimum1.11
Maximum4.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:54.226522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.11
5-th percentile1.2
Q11.39
median1.56
Q31.87
95-th percentile4.34
Maximum4.88
Range3.77
Interquartile range (IQR)0.475

Descriptive statistics

Standard deviation0.957
Coefficient of variation (CV)0.49
Kurtosis2.16
Mean1.95
Median Absolute Deviation (MAD)0.191
Skewness1.84
Sum437
Variance0.915
MonotonicityNot monotonic
2024-07-17T14:46:54.359581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.383565412 3
 
1.3%
1.59862594 3
 
1.3%
4.06539361 3
 
1.3%
2.768987073 3
 
1.3%
4.433768721 3
 
1.3%
3.554623358 3
 
1.3%
3.644729376 3
 
1.3%
1.520699924 3
 
1.3%
1.640740193 3
 
1.3%
1.604550548 3
 
1.3%
Other values (112) 194
86.6%
ValueCountFrequency (%)
1.11 1
0.4%
1.13 1
0.4%
1.14 1
0.4%
1.15 2
0.9%
1.16 1
0.4%
ValueCountFrequency (%)
4.877836022 3
1.3%
4.867951702 3
1.3%
4.433768721 3
1.3%
4.377576492 3
1.3%
4.105486918 3
1.3%

Manganese
Real number (ℝ)

Distinct119
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness1.18
Mean75.3
Minimum36.6
Maximum172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:54.495533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum36.6
5-th percentile37.4
Q152.6
median63.1
Q387.2
95-th percentile156
Maximum172
Range135
Interquartile range (IQR)34.7

Descriptive statistics

Standard deviation34.6
Coefficient of variation (CV)0.459
Kurtosis0.693
Mean75.3
Median Absolute Deviation (MAD)20
Skewness1.18
Sum1.69 × 104
Variance1.2 × 103
MonotonicityNot monotonic
2024-07-17T14:46:54.638079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
38.25065016 3
 
1.3%
52.26575058 3
 
1.3%
154.4911067 3
 
1.3%
78.21616474 3
 
1.3%
146.870421 3
 
1.3%
113.553875 3
 
1.3%
108.5352891 3
 
1.3%
61.98044201 3
 
1.3%
53.66650809 3
 
1.3%
53.81535233 3
 
1.3%
Other values (109) 194
86.6%
ValueCountFrequency (%)
36.61180812 3
1.3%
36.97949549 3
1.3%
37.13064744 3
1.3%
37.25579813 3
1.3%
38.08574146 3
1.3%
ValueCountFrequency (%)
171.6544205 3
1.3%
163.266363 3
1.3%
161.8855082 3
1.3%
156.5276373 3
1.3%
154.4911067 3
1.3%

Phosphorus
Real number (ℝ)

Distinct121
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness0.434
Mean2.61
Minimum1.82
Maximum3.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:54.773724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.82
5-th percentile2.12
Q12.41
median2.56
Q32.79
95-th percentile3.11
Maximum3.67
Range1.85
Interquartile range (IQR)0.388

Descriptive statistics

Standard deviation0.32
Coefficient of variation (CV)0.122
Kurtosis0.571
Mean2.61
Median Absolute Deviation (MAD)0.197
Skewness0.434
Sum585
Variance0.102
MonotonicityNot monotonic
2024-07-17T14:46:55.229819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2.388952538 3
 
1.3%
2.79427126 3
 
1.3%
2.598232872 3
 
1.3%
2.270431109 3
 
1.3%
2.698793564 3
 
1.3%
2.967753767 3
 
1.3%
3.230220163 3
 
1.3%
2.950481026 3
 
1.3%
2.789897817 3
 
1.3%
2.465212158 3
 
1.3%
Other values (111) 194
86.6%
ValueCountFrequency (%)
1.815367814 1
 
0.4%
1.899489237 1
 
0.4%
1.971070742 3
1.3%
1.991578228 3
1.3%
2.001845525 3
1.3%
ValueCountFrequency (%)
3.667507058 1
0.4%
3.496827834 1
0.4%
3.494090767 1
0.4%
3.493771582 1
0.4%
3.473233871 1
0.4%

Potassium
Real number (ℝ)

Distinct119
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness0.316
Mean16.7
Minimum10.7
Maximum25.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:55.379727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10.7
5-th percentile11.4
Q113.6
median16.7
Q319.3
95-th percentile23.1
Maximum25.3
Range14.5
Interquartile range (IQR)5.66

Descriptive statistics

Standard deviation3.69
Coefficient of variation (CV)0.221
Kurtosis-0.843
Mean16.7
Median Absolute Deviation (MAD)2.89
Skewness0.316
Sum3.74 × 103
Variance13.6
MonotonicityNot monotonic
2024-07-17T14:46:55.501957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
12.14724686 3
 
1.3%
19.31982984 3
 
1.3%
11.54495646 3
 
1.3%
18.63323975 3
 
1.3%
12.32287114 3
 
1.3%
13.13086666 3
 
1.3%
16.7996731 3
 
1.3%
15.57481219 3
 
1.3%
14.33323155 3
 
1.3%
13.86127534 3
 
1.3%
Other values (109) 194
86.6%
ValueCountFrequency (%)
10.74377858 3
1.3%
11.15639543 3
1.3%
11.18303607 3
1.3%
11.37237343 3
1.3%
11.54495646 3
1.3%
ValueCountFrequency (%)
25.26420519 1
0.4%
25.24678824 1
0.4%
24.85284507 1
0.4%
24.72074512 1
0.4%
24.65012505 1
0.4%

Sodium
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct38
Distinct (%)23.8%
Missing64
Missing (%)28.6%
Infinite0
Infinite (%)0.0%
Skewness1.29
Mean0.397
Minimum0.04
Maximum1.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:55.639783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.04
Q10.04
median0.0644
Q30.772
95-th percentile1.63
Maximum1.87
Range1.83
Interquartile range (IQR)0.732

Descriptive statistics

Standard deviation0.554
Coefficient of variation (CV)1.4
Kurtosis0.196
Mean0.397
Median Absolute Deviation (MAD)0.0244
Skewness1.29
Sum63.5
Variance0.307
MonotonicityNot monotonic
2024-07-17T14:46:55.770270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.04 69
30.8%
0.8395342335 3
 
1.3%
0.1351586783 3
 
1.3%
0.1027387017 3
 
1.3%
0.06439220897 3
 
1.3%
0.08822955372 3
 
1.3%
1.873600882 3
 
1.3%
1.632952304 3
 
1.3%
1.444682149 3
 
1.3%
0.8273818581 3
 
1.3%
Other values (28) 64
28.6%
(Missing) 64
28.6%
ValueCountFrequency (%)
0.04 69
30.8%
0.04610750719 3
 
1.3%
0.0494560129 3
 
1.3%
0.05221465838 3
 
1.3%
0.05603073286 1
 
0.4%
ValueCountFrequency (%)
1.873600882 3
1.3%
1.68938402 3
1.3%
1.632952304 3
1.3%
1.501831491 3
1.3%
1.444682149 3
1.3%

Sulphur
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct124
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness1.43
Mean4.92
Minimum1.54
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:55.902056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.54
5-th percentile1.83
Q12.18
median3.57
Q34.23
95-th percentile13.9
Maximum15
Range13.5
Interquartile range (IQR)2.06

Descriptive statistics

Standard deviation3.9
Coefficient of variation (CV)0.793
Kurtosis0.572
Mean4.92
Median Absolute Deviation (MAD)1.33
Skewness1.43
Sum1.1 × 103
Variance15.2
MonotonicityNot monotonic
2024-07-17T14:46:56.047011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3.203074683 3
 
1.3%
3.612306869 3
 
1.3%
13.4510359 3
 
1.3%
9.911388398 3
 
1.3%
14.10430763 3
 
1.3%
9.862373542 3
 
1.3%
9.185671024 3
 
1.3%
3.772658585 3
 
1.3%
3.943493642 3
 
1.3%
4.127053972 3
 
1.3%
Other values (114) 194
86.6%
ValueCountFrequency (%)
1.54 1
0.4%
1.56 1
0.4%
1.59 1
0.4%
1.64 1
0.4%
1.65 1
0.4%
ValueCountFrequency (%)
15.04861521 3
1.3%
14.22621597 3
1.3%
14.10430763 3
1.3%
13.97854032 3
1.3%
13.62673072 3
1.3%

Zinc
Real number (ℝ)

Distinct112
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Skewness0.398
Mean31.8
Minimum10.5
Maximum62.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-07-17T14:46:56.169559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile13.2
Q121
median31.2
Q340.3
95-th percentile54.9
Maximum62.4
Range51.9
Interquartile range (IQR)19.3

Descriptive statistics

Standard deviation12.6
Coefficient of variation (CV)0.397
Kurtosis-0.59
Mean31.8
Median Absolute Deviation (MAD)9.95
Skewness0.398
Sum7.12 × 103
Variance160
MonotonicityNot monotonic
2024-07-17T14:46:56.304085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
39 4
 
1.8%
13.00302465 3
 
1.3%
19.91381962 3
 
1.3%
35.37323704 3
 
1.3%
37.19703273 3
 
1.3%
24.62372265 3
 
1.3%
27.53150032 3
 
1.3%
20.98662354 3
 
1.3%
21.2497948 3
 
1.3%
27.51943679 3
 
1.3%
Other values (102) 193
86.2%
ValueCountFrequency (%)
10.53686219 3
1.3%
12.83834724 3
1.3%
12.84458977 3
1.3%
13.00302465 3
1.3%
14.56107752 3
1.3%
ValueCountFrequency (%)
62.44608273 1
 
0.4%
62.38500597 3
1.3%
60.12956138 3
1.3%
56.3761183 1
 
0.4%
55.02043149 3
1.3%

Interactions

2024-07-17T14:46:47.341350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:04.957148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:07.760896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:10.169063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:13.076353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:15.581462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:18.288299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:20.816392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:23.698654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:26.100423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:28.910181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:31.754045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:34.382193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:36.851214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:39.774447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:42.147118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:44.569802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:47.495415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:05.124016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:07.922587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:10.332880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:13.284516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:15.755634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:18.465245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:20.980438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:23.861289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:26.281053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:29.083986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:31.934404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:34.540652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:37.019621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:39.939063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:42.294231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:44.723356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:47.616147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:05.267073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:08.047245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:10.477868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:13.418202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:15.887159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:18.598662image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:21.430742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:23.982258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:26.432362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:29.227744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:32.076722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:34.673457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:37.167365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:40.069234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:42.423321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:44.849403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:47.748928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:05.429667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:08.188985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:10.615691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:13.566761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:16.039648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:18.749418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:21.577826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:24.126338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:26.593688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:29.382349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:32.234796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:34.815689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:37.321470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:40.215008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:42.564138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:44.989380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:47.874298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:05.583211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:08.319882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:10.750757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:13.703844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:16.199268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:18.893725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:21.725670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:24.263911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:26.749584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:29.526159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:32.384387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:34.948709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:37.472634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:40.351326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:42.694691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:45.119075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:48.015456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:05.748371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:08.469610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:10.906208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:13.857287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:16.361777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:19.051327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:21.881497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:24.410993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:26.933059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:29.681850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:32.541984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:35.096952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:37.632201image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:40.504447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:42.831937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:45.271803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:48.149649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:05.902848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:08.600071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:11.053596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:14.000310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:16.511266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:19.197309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:22.028158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:24.546260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:27.095282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:29.830305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:32.691073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:35.233997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:37.780498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:40.645215image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:42.966712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:45.417810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:48.281781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:06.065274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:08.734130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:11.199674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:14.137475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:16.661668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:19.337629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:22.166376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:24.674841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:27.250677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:29.965351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:32.845553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:35.371774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:37.925855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:40.780592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:43.097671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:45.557819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:48.402494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:06.210365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:08.857875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:11.328085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:14.260239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:16.812819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:19.462362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:22.297447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:24.795804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:27.398636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:30.088516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:32.983398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:35.490667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:38.063917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:40.908228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:43.217678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:45.680952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:48.562176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:06.386195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:09.008092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:11.489601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:14.415691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:16.986811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:19.621508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:22.460463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:24.949857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:27.574605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:30.241661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:33.155153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:35.649644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:38.229312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:41.055636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:43.369750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:45.841747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:48.694544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:06.580799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:09.149359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:11.647815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:14.560874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:17.148229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:19.764269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:22.616437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:25.091711image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:27.739579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:30.707391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:33.312486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:35.791649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:38.389671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:41.189051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:43.518114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:45.983768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:48.842118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:06.767060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:09.309473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:11.812152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:14.713224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:17.325566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:19.920491image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:22.780750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:25.251924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:27.919920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:30.857439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:33.476168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:35.957995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:38.556939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:41.335736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:43.666050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:46.138423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:48.973019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:06.933589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:09.450545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:11.978956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:14.851499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:17.481074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:20.065994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:22.928411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:25.396340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:28.082671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:30.992696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:33.620915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:36.098100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:39.021653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:41.467287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:43.816660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:46.273710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:49.120273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:07.116846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:09.601169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:12.139734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:15.004579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:17.654840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:20.229536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:23.087664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:25.546308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:28.269469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:31.144413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:33.782593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:36.271213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:39.183070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:41.614621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:43.969301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:46.432155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:49.255628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:07.280910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:09.742070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:12.631394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:15.148941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:17.817550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:20.374234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:23.256365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:25.680300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:28.426332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:31.292645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:33.935369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:36.420773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:39.330439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:41.743816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:44.116851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:46.567535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:49.397284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:07.449386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:09.891320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:12.787888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:15.303919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:17.973900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:20.527496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:23.410845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:25.821213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:28.592795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:31.450162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:34.090431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:36.570890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:39.478550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:41.888070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:44.283827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:47.017147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:49.527043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:07.609451image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:10.037017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:12.938030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:15.446216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:18.130959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:20.675707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:23.559049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:25.964866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:28.752921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:31.610801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:34.241706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:36.714411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:39.626679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:42.019450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:44.438067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-07-17T14:46:47.198627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-07-17T14:46:56.478602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Experimental_YearPlot_IDSDMSFMTotal_CarbonTotal_NitrogenCN_RatioCalciumCopperIronMagnesiumManganesePhosphorusPotassiumSodiumSulphurZincDateCropBeneficials
Experimental_Year1.000-0.1160.3680.3660.073-0.5650.627-0.1480.6050.292-0.3470.3320.3720.587NaN-0.7460.4660.9930.5950.640
Plot_ID-0.1161.0000.0080.056-0.0160.085-0.1190.1180.0310.0730.0360.0920.008-0.008-0.0240.1120.0170.5430.4860.210
SDM0.3680.0081.0000.924-0.491-0.6590.5290.2680.2710.7650.3480.7520.2210.4730.609-0.1350.7360.5000.7080.491
SFM0.3660.0560.9241.000-0.542-0.6570.5200.3270.3170.7830.2900.7990.2290.4970.616-0.1770.7620.6210.7120.365
Total_Carbon0.073-0.016-0.491-0.5421.0000.514-0.322-0.5710.408-0.479-0.293-0.5940.0900.149-0.740-0.275-0.2710.5900.7350.259
Total_Nitrogen-0.5650.085-0.659-0.6570.5141.000-0.961-0.173-0.116-0.482-0.093-0.4820.047-0.259-0.5170.393-0.4770.6190.6670.414
CN_Ratio0.627-0.1190.5290.520-0.322-0.9611.000-0.0280.2010.332-0.0900.277-0.1070.2520.231-0.5660.3520.5900.6300.537
Calcium-0.1480.1180.2680.327-0.571-0.173-0.0281.000-0.2820.4010.4940.6230.013-0.1900.6580.5340.2990.4810.6970.166
Copper0.6050.0310.2710.3170.408-0.1160.201-0.2821.0000.372-0.1480.2380.3730.845-0.308-0.6490.5610.5470.6580.431
Iron0.2920.0730.7650.783-0.479-0.4820.3320.4010.3721.0000.3670.8170.2520.5470.547-0.0870.7750.5820.6410.352
Magnesium-0.3470.0360.3480.290-0.293-0.093-0.0900.494-0.1480.3671.0000.4260.1460.0890.7500.5710.4690.5230.6700.119
Manganese0.3320.0920.7520.799-0.594-0.4820.2770.6230.2380.8170.4261.0000.3920.4490.6700.1050.8050.6570.7580.400
Phosphorus0.3720.0080.2210.2290.0900.047-0.1070.0130.3730.2520.1460.3921.0000.4600.222-0.1180.4110.3970.3950.355
Potassium0.587-0.0080.4730.4970.149-0.2590.252-0.1900.8450.5470.0890.4490.4601.0000.062-0.5150.7240.6230.6550.398
SodiumNaN-0.0240.6090.616-0.740-0.5170.2310.658-0.3080.5470.7500.6700.2220.0621.0000.5590.7010.5630.7081.000
Sulphur-0.7460.112-0.135-0.177-0.2750.393-0.5660.534-0.649-0.0870.5710.105-0.118-0.5150.5591.000-0.1760.6180.9770.475
Zinc0.4660.0170.7360.762-0.271-0.4770.3520.2990.5610.7750.4690.8050.4110.7240.701-0.1761.0000.5640.7260.403
Date0.9930.5430.5000.6210.5900.6190.5900.4810.5470.5820.5230.6570.3970.6230.5630.6180.5641.0000.9930.629
Crop0.5950.4860.7080.7120.7350.6670.6300.6970.6580.6410.6700.7580.3950.6550.7080.9770.7260.9931.0000.541
Beneficials0.6400.2100.4910.3650.2590.4140.5370.1660.4310.3520.1190.4000.3550.3981.0000.4750.4030.6290.5411.000

Missing values

2024-07-17T14:46:49.754460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-17T14:46:50.190321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-17T14:46:50.436857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Experimental_YearDatePlot_IDCropBeneficialsSDMSFMTotal_CarbonTotal_NitrogenCN_RatioCalciumCopperIronMagnesiumManganesePhosphorusPotassiumSodiumSulphurZinc
020192019-05-21 00:00:00.00000001Winter wheatControl83.03349.600000446.59040534.62292612.8986915.2182862.94253251.4880141.38356538.2506502.38895312.1472470.0645413.20307513.003025
120192019-05-21 00:00:00.00000002Winter wheatControl100.33438.000000450.35451937.04491012.1570635.2842673.24442961.9677281.38152438.7187352.29221013.6796180.0400003.61230714.561078
220192019-05-21 00:00:00.00000003Winter wheatControl76.7820.768622451.81541435.47870812.7348336.2205523.69629056.5806731.55787239.7935001.99157812.0468890.0943393.72086120.768622
320192019-05-21 00:00:00.00000004Winter wheatControl136.1016.064705449.16471537.43417111.9988185.2210394.569095177.4614381.44261244.4547942.34230713.7986570.0400003.31980316.064705
420192019-05-21 00:00:00.00000005Winter wheatControl75.50305.300000445.89860934.68357812.8561864.9974912.78468151.9937731.42189836.9794952.40650811.1830360.0400003.07339810.536862
520192019-05-21 00:00:00.00000006Winter wheatControl109.12482.600000450.97833637.93882411.8870724.7893843.72548061.4352641.35732238.0857412.45900714.0473390.0494563.46811915.160778
620192019-05-21 00:00:00.00000007Winter wheatControl77.06333.800000447.81875635.19781112.7230986.0083413.75454253.8258181.54422737.2557981.97107111.8959720.0400003.87593815.836747
720192019-05-21 00:00:00.00000008Winter wheatControl106.75496.700000452.33070437.39524412.0959884.9369564.63663263.3291181.33530236.6118082.38265912.9263280.0400003.26032119.640758
820192019-05-21 00:00:00.00000009Winter wheatControl124.26536.000000444.58681135.07634912.6747634.8106493.36428050.6235351.36579039.4800242.39743712.0697940.0522153.12549512.844590
920192019-05-21 00:00:00.000000010Winter wheatControl105.20499.500000450.92062038.67810611.6583404.3425563.43485561.3351291.36974345.4880142.49710414.8786090.0400003.52299016.481238
Experimental_YearDatePlot_IDCropBeneficialsSDMSFMTotal_CarbonTotal_NitrogenCN_RatioCalciumCopperIronMagnesiumManganesePhosphorusPotassiumSodiumSulphurZinc
21420212021-07-26 00:00:00.0000000104Grain maizeBMs258.01535.8442.124.617.9715455.586.2785.01.1978.02.7717.5NaN1.7934.0
21520212021-07-26 00:00:00.0000000104Grain maizeControl317.81992.4442.025.017.6800005.666.9094.01.2770.02.9118.6NaN1.8532.0
21620212021-07-26 00:00:00.0000000177Grain maizeBMs471.02786.3448.329.914.9933116.658.22102.01.4289.02.5718.2NaN1.8341.0
21720212021-07-26 00:00:00.0000000177Grain maizeControl370.81941.0450.829.515.2813566.678.75105.01.4188.02.5618.2NaN1.6539.0
21820212021-07-26 00:00:00.0000000178Grain maizeBMs380.32442.4442.331.114.2218657.028.79106.01.29123.02.7918.8NaN2.0639.0
21920212021-07-26 00:00:00.0000000178Grain maizeControl431.82736.5444.431.614.0632916.398.80102.01.17112.02.7517.4NaN1.8442.0
22020212021-07-26 00:00:00.0000000179Grain maizeBMs367.52294.8442.625.717.2217904.936.6294.01.1389.02.6717.9NaN3.0634.0
22120212021-07-26 00:00:00.0000000179Grain maizeControl541.82246.5437.624.118.1576765.416.2795.01.14105.02.6518.3NaN2.0838.0
22220212021-07-26 00:00:00.0000000180Grain maizeBMs314.01852.5439.723.918.3974905.536.2086.01.2077.02.7317.5NaN1.8635.0
22320212021-07-26 00:00:00.0000000180Grain maizeControl366.12232.2443.526.416.7992426.417.9090.01.3786.02.5317.8NaN1.8639.0